Source code for species.plot.plot_color

"""
Module with functions for creating plots of
color-magnitude  color-color diagrams.
"""

import json
import os
import pathlib
import warnings

from typing import Dict, List, Optional, Tuple, Union

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

from matplotlib.colorbar import Colorbar
from matplotlib.ticker import MultipleLocator
from scipy.interpolate import interp1d
from typeguard import typechecked

from species.core.box import IsochroneBox, ColorMagBox, ColorColorBox
from species.read.read_filter import ReadFilter
from species.read.read_object import ReadObject
from species.util.core_util import print_section
from species.util.dust_util import calc_reddening, ism_extinction
from species.util.model_util import convert_model_name
from species.util.plot_util import (
    field_bounds_ticks,
    sptype_to_index,
    remove_color_duplicates,
)


[docs] @typechecked def plot_color_magnitude( boxes: list, objects: Optional[ Union[ List[Tuple[str, str, str, str]], List[Tuple[str, str, str, str, Optional[dict], Optional[dict]]], ] ] = None, mass_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None, teff_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None, companion_labels: bool = False, accretion: bool = False, reddening: Optional[ List[Tuple[Tuple[str, str], Tuple[str, float], str, float, Tuple[float, float]]] ] = None, ism_red: Optional[ List[Tuple[Tuple[str, str], str, float, Tuple[float, float]]] ] = None, field_range: Optional[Tuple[str, str]] = None, label_x: str = "Color", label_y: str = "Absolute magnitude", xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, offset: Optional[Tuple[float, float]] = None, legend: Optional[Union[str, dict, Tuple[float, float]]] = "upper left", figsize: Optional[Tuple[float, float]] = (4.0, 4.8), output: Optional[str] = None, ) -> mpl.figure.Figure: """ Function for creating a color-magnitude diagram. Parameters ---------- boxes : list(species.core.box.ColorMagBox, species.core.box.IsochroneBox) Boxes with the color-magnitude and isochrone data from photometric libraries, spectral libraries, and/or atmospheric models. The synthetic data have to be created with :func:`~species.read.read_isochrone.ReadIsochrone.get_color_magnitude`. These boxes contain synthetic colors and magnitudes for a given age and a range of masses. objects : list(tuple(str, str, str, str)), list(tuple(str, str, str, str, dict, dict)), None Tuple with individual objects. The objects require a tuple with their database tag, the two filter names for the color, and the filter name for the absolute magnitude. Optionally, a dictionary with keyword arguments can be provided for the object's marker and label, respectively. For example, ``{'marker': 'o', 'ms': 10}`` for the marker and ``{'ha': 'left', 'va': 'bottom', 'xytext': (5, 5)})`` for the label. The parameter is not used if set to ``None``. mass_labels : dict(str, list(tuple(float, str))), None Plot labels with masses next to the isochrone data. The argument is a dictionary. The keys are the isochrone tags and the values are lists of tuples. Each tuple contains the mass in :math:`M_\\mathrm{J}` and the position of the label ('left' or 'right), for example ``{'sonora+0.5': [(10., 'left'), (20., 'right')]}``. No labels will be shown if the argument is set to ``None`` or if an isochrone tag is not included in the dictionary. The tags are stored as the ``iso_tag`` attribute of each :class:`~species.core.box.ColorColorBox`. teff_labels : dict(str, list(tuple(float, str))), None Plot labels with temperatures (K) next to the isochrones. The argument is a dictionary with the names of the models and a list with tuples that contain the effective temperatures and 'left' or 'right' to indicate the position of the label (so similar to the use of mass_labels``). For example, ``{'planck': [(1000., 'left'), (1200., 'right')]``. No labels are shown if the argument is set to ``None``. companion_labels : bool Plot labels with the names of the directly imaged companions. accretion : bool Plot accreting, directly imaged objects with a different symbol than the regular, directly imaged objects. The object names from ``objects`` will be compared with the data from `data/companion_data/companion_data.json` to check if a companion is accreting or not. reddening : list(tuple(tuple(str, str), tuple(str, float), str, float, tuple(float, float))), None Include reddening arrows by providing a list with tuples. Each tuple contains the filter names for the color, the filter name and value of the magnitude, the mean particle radius (um), and the start position (color, mag) of the arrow in the plot, so ``((filter_color_1, filter_color_2), (filter_mag, mag_value), composition, radius, (x_pos, y_pos))``. The composition can be either ``'Fe'`` or ``'MgSiO3'`` (both with crystalline structure). A log-normal size distribution is used with the specified mean radius and the geometric standard deviation is fixed to 2. Both ``xlim`` and ``ylim`` need to be set for the correct rotation of the reddening label. The parameter is not used if set to ``None``. ism_red : list(tuple(tuple(str, str), str, float, tuple(float, float))), None List with reddening arrows for ISM extinction. Each item in the list is a tuple that itself contain a tuple with the filter names for the color, the filter name of the magnitude, the visual extinction, and the start position (color, mag) of the arrow in the plot, so ``((filter_color_1, filter_color_2), filter_mag, A_V, (x_pos, y_pos))``. The parameter is not used if the argument is set to ``None``. field_range : tuple(str, str), None Range of spectral types for which the field objects will be plotted and labeled at the discrete colorbar. The tuple should contain the lower and upper value of either the classes, for example ('K', 'T'), or as subclasses by including early/late, for example ('late K', 'early T'). The range is set to the default of 'early M' to 'early Y' if the argument is set to ``None``. label_x : str Label for the x-axis. label_y : str Label for the y-axis. xlim : tuple(float, float), None Limits for the x-axis. Not used if set to None. ylim : tuple(float, float), None Limits for the y-axis. Not used if set to None. offset : tuple(float, float), None Offset of the x- and y-axis label. legend : str, tuple(float, float), dict, None Legend position or keyword arguments. No legend is shown if set to ``None``. figsize : tuple(float, float) Figure size. output : str, None Output filename for the plot. The plot is shown in an interface window if the argument is set to ``None``. Returns ------- matplotlib.figure.Figure The ``Figure`` object that can be used for further customization of the plot. """ print_section("Plot color-magnitude diagram") print("Boxes:") for item in boxes: if isinstance(item, list): item_type = item[0].__class__.__name__ print(f" - List with {len(item)} x {item_type}") else: print(f" - {item.__class__.__name__}") if objects is None: print(f"\nObjects: {objects}") else: print("\nObjects:") for item in objects: print(f" - {item[0]}: {item[1:]}") print(f"\nSpectral range: {field_range}") print(f"Companion labels: {companion_labels}") print(f"Accretion markers: {accretion}") print(f"\nMass labels: {mass_labels}") print(f"Teff labels: {teff_labels}") if reddening is None: print(f"\nReddening: {reddening}") else: print("\nReddening:") for item in reddening: print(f" - {item}") print(f"\nFigure size: {figsize}") print(f"Limits x axis: {xlim}") print(f"Limits y axis: {ylim}") plt.rcParams["font.family"] = "serif" plt.rcParams["mathtext.fontset"] = "dejavuserif" # model_color = ("#234398", "#f6a432", "black") model_color = ( "tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple", "tab:brown", "tab:pink", "tab:olive", "tab:cyan", ) model_linestyle = ("-", "--", ":", "-.") if field_range is None: field_range = ("early M", "early Y") if (len(field_range[0]) == 1 and len(field_range[1]) != 1) or ( len(field_range[0]) != 1 and len(field_range[1]) == 1 ): raise ValueError( "The argument of 'field_range' should " "be a tuple with either the range of " "spectral type classes, for example " "('A', 'M'), or a tuple with the range " "of subclasses, for example ('early K', " "'late L')." ) if len(field_range[0]) == 1 and len(field_range[1]) == 1: check_subclass = False else: check_subclass = True isochrones = [] planck = [] models = [] empirical = [] for item in boxes: if isinstance(item, IsochroneBox): isochrones.append(item) elif isinstance(item, ColorMagBox): if item.object_type == "model": models.append(item) elif item.library == "planck": planck.append(item) else: empirical.append(item) else: raise ValueError( f"Found a {type(item)} while only ColorMagBox and " "IsochroneBox objects can be provided to 'boxes'." ) if empirical: fig = plt.figure(figsize=figsize) gridsp = mpl.gridspec.GridSpec(3, 1, height_ratios=[0.2, 0.1, 4.5]) gridsp.update(wspace=0.0, hspace=0.0, left=0, right=1, bottom=0, top=1) ax1 = plt.subplot(gridsp[2, 0]) ax2 = plt.subplot(gridsp[0, 0]) else: fig = plt.figure(figsize=figsize) gridsp = mpl.gridspec.GridSpec(1, 1) gridsp.update(wspace=0.0, hspace=0.0, left=0, right=1, bottom=0, top=1) ax1 = plt.subplot(gridsp[0, 0]) ax1.tick_params( axis="both", which="major", colors="black", labelcolor="black", direction="in", width=1, length=5, labelsize=12, top=True, bottom=True, left=True, right=True, ) ax1.tick_params( axis="both", which="minor", colors="black", labelcolor="black", direction="in", width=1, length=3, labelsize=12, top=True, bottom=True, left=True, right=True, ) ax1.xaxis.set_major_locator(MultipleLocator(1.0)) ax1.yaxis.set_major_locator(MultipleLocator(1.0)) ax1.xaxis.set_minor_locator(MultipleLocator(0.2)) ax1.yaxis.set_minor_locator(MultipleLocator(0.2)) ax1.set_xlabel(label_x, fontsize=14) ax1.set_ylabel(label_y, fontsize=14) ax1.invert_yaxis() if offset is not None: ax1.get_xaxis().set_label_coords(0.5, offset[0]) ax1.get_yaxis().set_label_coords(offset[1], 0.5) else: ax1.get_xaxis().set_label_coords(0.5, -0.08) ax1.get_yaxis().set_label_coords(-0.12, 0.5) if xlim is not None: ax1.set_xlim(xlim[0], xlim[1]) if ylim is not None: ax1.set_ylim(ylim[0], ylim[1]) if models is not None: count = 0 model_dict = {} for j, item in enumerate(models): if item.library == "sonora-bobcat": model_key = item.library + item.iso_tag[-4:] else: model_key = item.library if model_key not in model_dict: model_dict[model_key] = [count, 0] count += 1 else: model_dict[model_key] = [ model_dict[model_key][0], model_dict[model_key][1] + 1, ] model_count = model_dict[model_key] if model_count[1] == 0: label = convert_model_name(item.library) if item.library == "sonora-bobcat": metal = float(item.iso_tag[-4:]) label += f", [M/H] = {metal}" if item.library == "zhu2015": ax1.plot( item.color, item.magnitude, marker="x", ms=5, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color="gray", label=label, zorder=0, ) xlim = ax1.get_xlim() ylim = ax1.get_ylim() for i, teff_item in enumerate(item.sptype): teff_label = ( rf"{teff_item:.0e} $M_\mathregular{{Jup}}^{2}$ yr$^{{-1}}$" ) if item.magnitude[i] > ylim[1]: ax1.annotate( teff_label, (item.color[i], item.magnitude[i]), color="gray", fontsize=8, ha="left", va="center", xytext=(item.color[i] + 0.1, item.magnitude[i] + 0.05), zorder=3, ) else: ax1.plot( item.color, item.magnitude, linestyle=model_linestyle[model_count[1]], lw=1.0, color=model_color[model_count[0]], label=label, zorder=0, ) if mass_labels is not None: interp_magnitude = interp1d(item.mass, item.magnitude) interp_color = interp1d(item.mass, item.color) if item.iso_tag in mass_labels: label_select = mass_labels[item.iso_tag] else: label_select = [] for i, mass_item in enumerate(label_select): if isinstance(mass_item, tuple): mass_val = mass_item[0] mass_pos = mass_item[1] else: mass_val = mass_item mass_pos = "right" pos_color = interp_color(mass_val) pos_mag = interp_magnitude(mass_val) # if j == 1 and mass_val == 10.: # mass_ha = "center" # mass_xytext = (pos_color, pos_mag-0.2) if mass_pos == "left": mass_ha = "right" mass_xytext = (pos_color - 0.05, pos_mag) else: mass_ha = "left" mass_xytext = (pos_color + 0.05, pos_mag) mass_label = str(int(mass_val)) + r" M$_\mathregular{J}$" xlim = ax1.get_xlim() ylim = ax1.get_ylim() if ( xlim[0] + 0.2 < pos_color < xlim[1] - 0.2 and ylim[1] + 0.2 < pos_mag < ylim[0] - 0.2 ): ax1.scatter( pos_color, pos_mag, c=model_color[model_count[0]], s=15, edgecolor="none", zorder=0, ) ax1.annotate( mass_label, (pos_color, pos_mag), color=model_color[model_count[0]], fontsize=9, xytext=mass_xytext, zorder=3, ha=mass_ha, va="center", ) else: ax1.plot( item.color, item.magnitude, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color=model_color[model_count[0]], zorder=0, ) if planck is not None: planck_count = 0 for j, item in enumerate(planck): if planck_count == 0: label = convert_model_name(item.library) else: label = None ax1.plot( item.color, item.magnitude, linestyle="--", linewidth=0.8, color="gray", label=label, zorder=0, ) if teff_labels is not None and planck_count == 0: interp_magnitude = interp1d(item.sptype, item.magnitude) interp_color = interp1d(item.sptype, item.color) if item.library in teff_labels: label_select = teff_labels[item.library] else: label_select = [] for i, teff_item in enumerate(label_select): if isinstance(teff_item, tuple): teff_val = teff_item[0] teff_pos = teff_item[1] else: teff_val = teff_item teff_pos = "right" if j == 0 or (j > 0 and teff_val < 20.0): pos_color = interp_color(teff_val) pos_mag = interp_magnitude(teff_val) if teff_pos == "left": teff_ha = "right" teff_xytext = (pos_color - 0.05, pos_mag) else: teff_ha = "left" teff_xytext = (pos_color + 0.05, pos_mag) teff_label = f"{int(teff_val)} K" xlim = ax1.get_xlim() ylim = ax1.get_ylim() if ( xlim[0] + 0.2 < pos_color < xlim[1] - 0.2 and ylim[1] + 0.2 < pos_mag < ylim[0] - 0.2 ): ax1.scatter( pos_color, pos_mag, c="gray", s=15, ec="none", zorder=0 ) if planck_count == 0: ax1.annotate( teff_label, (pos_color, pos_mag), color="gray", fontsize=9, xytext=teff_xytext, zorder=3, ha=teff_ha, va="center", ) planck_count += 1 if empirical: cmap = plt.cm.viridis bounds, ticks, ticklabels = field_bounds_ticks(field_range, check_subclass) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) for item in empirical: sptype = item.sptype color = item.color magnitude = item.magnitude names = item.names if isinstance(sptype, list): sptype = np.array(sptype) if item.object_type in ["field", None]: indices = np.where(sptype != "None")[0] sptype = sptype[indices] color = color[indices] magnitude = magnitude[indices] spt_disc = sptype_to_index(field_range, sptype, check_subclass) _, unique = np.unique(color, return_index=True) sptype = sptype[unique] color = color[unique] magnitude = magnitude[unique] spt_disc = spt_disc[unique] scat = ax1.scatter( color, magnitude, c=spt_disc, cmap=cmap, norm=norm, s=50, alpha=0.7, edgecolor="none", zorder=2, ) cb = Colorbar( ax=ax2, mappable=scat, orientation="horizontal", ticklocation="top", format="%.2f", ) cb.ax.tick_params( width=1, length=5, labelsize=10, direction="in", color="black" ) cb.set_ticks(ticks) cb.set_ticklabels(ticklabels) cb.ax.minorticks_off() elif item.object_type == "young": if objects is not None: object_names = [] for obj_item in objects: object_names.append(obj_item[0]) indices = remove_color_duplicates(object_names, names) color = color[indices] magnitude = magnitude[indices] ax1.plot( color, magnitude, marker="s", ms=4, linestyle="none", alpha=0.7, color="gray", markeredgecolor="black", label="Young/low-gravity", zorder=2, ) # for item in names[indices]: # # if item == '2MASSWJ2244316+204343': # item = '2MASS 2244+2043' # # kwargs = {'ha': 'left', 'va': 'center', 'fontsize': 8.5, # 'xytext': (5., 0.), 'color': 'black'} # # ax1.annotate(item, (color, magnitude), zorder=3, # textcoords='offset points', **kwargs) if isochrones: for item in isochrones: ax1.plot( item.color, item.magnitude, linestyle="-", linewidth=1.0, color="black" ) if reddening is not None: for item in reddening: ext_1, ext_2 = calc_reddening( item[0], item[1], composition=item[2], structure="crystalline", radius_g=item[3], ) delta_x = ext_1 - ext_2 delta_y = item[1][1] x_pos = item[4][0] + delta_x y_pos = item[4][1] + delta_y ax1.annotate( "", (x_pos, y_pos), xytext=(item[4][0], item[4][1]), fontsize=8, arrowprops={"arrowstyle": "->"}, color="black", zorder=3.0, ) x_pos_text = item[4][0] + delta_x / 2.0 y_pos_text = item[4][1] + delta_y / 2.0 vector_len = np.sqrt(delta_x**2 + delta_y**2) if item[2] == "MgSiO3": dust_species = r"MgSiO$_{3}$" elif item[2] == "Fe": dust_species = "Fe" if (item[3]).is_integer(): red_label = f"{dust_species} ({item[3]:.0f} µm)" else: red_label = f"{dust_species} ({item[3]:.1f} µm)" text = ax1.annotate( red_label, (x_pos_text, y_pos_text), xytext=(7.0 * delta_y / vector_len, 7.0 * delta_x / vector_len), textcoords="offset points", fontsize=8.0, color="black", ha="center", va="center", ) ax1.plot([item[4][0], x_pos], [item[4][1], y_pos], "-", color="white") sp1 = ax1.transData.transform_point((item[4][0], item[4][1])) sp2 = ax1.transData.transform_point((x_pos, y_pos)) angle = np.degrees(np.arctan2(sp2[1] - sp1[1], sp2[0] - sp1[0])) text.set_rotation(angle) if ism_red is not None: for item in ism_red: # Color filters read_filt_0 = ReadFilter(item[0][0]) read_filt_1 = ReadFilter(item[0][1]) # Magnitude filter read_filt_2 = ReadFilter(item[1]) mean_wavel = np.array( [ read_filt_0.mean_wavelength(), read_filt_1.mean_wavelength(), read_filt_2.mean_wavelength(), ] ) ext_mag = ism_extinction(item[2], 3.1, mean_wavel) delta_x = ext_mag[0] - ext_mag[1] delta_y = ext_mag[2] x_pos = item[3][0] + delta_x y_pos = item[3][1] + delta_y ax1.annotate( "", (x_pos, y_pos), xytext=(item[3][0], item[3][1]), fontsize=8, arrowprops={"arrowstyle": "->"}, color="black", zorder=3.0, ) x_pos_text = item[3][0] + delta_x / 2.0 y_pos_text = item[3][1] + delta_y / 2.0 vector_len = np.sqrt(delta_x**2 + delta_y**2) if (item[2]).is_integer(): red_label = rf"A$_\mathregular{{V}}$ = {item[2]:.0f}" else: red_label = rf"A$_\mathregular{{V}}$ = {item[2]:.1f}" text = ax1.annotate( red_label, (x_pos_text, y_pos_text), xytext=(8.0 * delta_y / vector_len, 8.0 * delta_x / vector_len), textcoords="offset points", fontsize=8.0, color="black", ha="center", va="center", ) ax1.plot([item[3][0], x_pos], [item[3][1], y_pos], "-", color="white") sp1 = ax1.transData.transform_point((item[3][0], item[3][1])) sp2 = ax1.transData.transform_point((x_pos, y_pos)) angle = np.degrees(np.arctan2(sp2[1] - sp1[1], sp2[0] - sp1[0])) text.set_rotation(angle) if objects is not None: for i, item in enumerate(objects): objdata = ReadObject(item[0]) objcolor1 = objdata.get_photometry(item[1]) objcolor2 = objdata.get_photometry(item[2]) if objcolor1.ndim == 2: print( f"Found {objcolor1.shape[1]} values for filter {item[1]} of {item[0]}" ) print( f"so using the first value: {objcolor1[0, 0]} +/- {objcolor1[1, 0]} mag" ) objcolor1 = objcolor1[:, 0] if objcolor2.ndim == 2: print( f"Found {objcolor2.shape[1]} values for filter {item[2]} of {item[0]}" ) print( f"so using the first value: {objcolor2[0, 0]} +/- {objcolor2[1, 0]} mag" ) objcolor2 = objcolor2[:, 0] abs_mag, abs_err = objdata.get_absmag(item[3]) if isinstance(abs_mag, np.ndarray): abs_mag = abs_mag[0] abs_err = abs_err[0] colorerr = np.sqrt(objcolor1[1] ** 2 + objcolor2[1] ** 2) x_color = objcolor1[0] - objcolor2[0] species_folder = str(pathlib.Path(__file__).parent.resolve())[:-4] data_file = os.path.join( species_folder, "data/companion_data/companion_data.json" ) with open(data_file, "r", encoding="utf-8") as json_file: comp_data = json.load(json_file) if len(item) > 4 and item[4] is not None: kwargs = item[4] else: kwargs = { "marker": "o", "ms": 5.0, "color": "black", "mfc": "white", "mec": "black", "label": "Companions", } if ( accretion and item[0] in comp_data and comp_data[item[0]]["accretion"] ): kwargs["marker"] = "X" kwargs["ms"] = 7.0 kwargs["label"] = "Accreting" ax1.errorbar( x_color, abs_mag, yerr=abs_err, xerr=colorerr, zorder=3, **kwargs ) if companion_labels: if len(item) > 4 and item[5] is not None: kwargs = item[5] else: kwargs = { "ha": "left", "va": "bottom", "fontsize": 8.5, "xytext": (5.0, 5.0), "color": "black", } ax1.annotate( objdata.object_name, (x_color, abs_mag), zorder=3, textcoords="offset points", **kwargs, ) if legend is not None: handles, labels = ax1.get_legend_handles_labels() # Prevent duplicates by_label = dict(zip(labels, handles)) if handles: if isinstance(legend, (str, tuple)): ax1.legend( by_label.values(), by_label.keys(), loc=legend, fontsize=8.5, frameon=False, numpoints=1, ) else: ax1.legend(by_label.values(), by_label.keys(), **legend) if output is None: plt.show() else: print(f"\nOutput: {output}") plt.savefig(output, bbox_inches="tight") return fig
[docs] @typechecked def plot_color_color( boxes: list, objects: Optional[ Union[ List[Tuple[str, Tuple[str, str], Tuple[str, str]]], List[ Tuple[ str, Tuple[str, str], Tuple[str, str], Optional[dict], Optional[dict], ] ], ] ] = None, mass_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None, teff_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None, companion_labels: bool = False, reddening: Optional[ List[ Tuple[ Tuple[str, str], Tuple[str, str], Tuple[str, float], str, float, Tuple[float, float], ] ] ] = None, field_range: Optional[Tuple[str, str]] = None, label_x: str = "Color", label_y: str = "Color", xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, offset: Optional[Tuple[float, float]] = None, legend: Optional[Union[str, dict, Tuple[float, float]]] = "upper left", figsize: Optional[Tuple[float, float]] = (4.0, 4.3), output: Optional[str] = None, ) -> mpl.figure.Figure: """ Function for creating a color-color diagram. Parameters ---------- boxes : list(species.core.box.ColorColorBox, species.core.box.IsochroneBox) Boxes with the color-color from photometric libraries, spectral libraries, isochrones, and/or atmospheric models. objects : tuple(tuple(str, tuple(str, str), tuple(str, str))), tuple(tuple(str, tuple(str, str), tuple(str, str), dict, dict)), None Tuple with individual objects. The objects require a tuple with their database tag, the two filter names for the first color, and the two filter names for the second color. Optionally, a dictionary with keyword arguments can be provided for the object's marker and label, respectively. For example, ``{'marker': 'o', 'ms': 10}`` for the marker and ``{'ha': 'left', 'va': 'bottom', 'xytext': (5, 5)})`` for the label. The parameter is not used if set to ``None``. mass_labels : dict(str, list(tuple(float, str))), None Plot labels with masses next to the isochrone data. The argument is a dictionary. The keys are the isochrone tags and the values are lists of tuples. Each tuple contains the mass in :math:`M_\\mathrm{J}` and the position of the label ('left' or 'right), for example ``{'sonora+0.5': [(10., 'left'), (20., 'right')]}``. No labels will be shown if the argument is set to ``None`` or if an isochrone tag is not included in the dictionary. The tags are stored as the ``iso_tag`` attribute of each :class:`~species.core.box.ColorColorBox`. teff_labels : dict(str, list(tuple(float, str))), None Plot labels with temperatures (K) next to the isochrones. The argument is a dictionary with the names of the models and a list with tuples that contain the effective temperatures and 'left' or 'right' to indicate the position of the label (so similar to the use of mass_labels``). For example, ``{'planck': [(1000., 'left'), (1200., 'right')]``. No labels are shown if the argument is set to ``None``. companion_labels : bool Plot labels with the names of the directly imaged companions. reddening : list(tuple(tuple(str, str), tuple(str, str), tuple(str, float), str, float, tuple(float, float)), None Include reddening arrows by providing a list with tuples. Each tuple contains the filter names for the color, the filter name for the magnitude, the particle radius (um), and the start position (color, mag) of the arrow in the plot, so (filter_color_1, filter_color_2, filter_mag, composition, radius, (x_pos, y_pos)). The composition can be either 'Fe' or 'MgSiO3' (both with crystalline structure). The parameter is not used if set to ``None``. field_range : tuple(str, str), None Range of spectral types for which the field objects will be plotted and labeled at the discrete colorbar. The tuple should contain the lower and upper value of either the classes, for example ('K', 'T'), or as subclasses by including early/late, for example ('late K', 'early T'). The range is set to the default of 'early M' to 'early Y' if the argument is set to ``None``. label_x : str Label for the x-axis. label_y : str Label for the y-axis. xlim : tuple(float, float) Limits for the x-axis. ylim : tuple(float, float) Limits for the y-axis. offset : tuple(float, float), None Offset of the x- and y-axis label. legend : str, tuple(float, float), dict, None Legend position or dictionary with keyword arguments. No legend is shown if the argument is set to ``None``. figsize : tuple(float, float) Figure size. output : str, None Output filename for the plot. The plot is shown in an interface window if the argument is set to ``None``. Returns ------- matplotlib.figure.Figure The ``Figure`` object that can be used for further customization of the plot. """ print_section("Plot color-color diagram") print("Boxes:") for item in boxes: if isinstance(item, list): item_type = item[0].__class__.__name__ print(f" - List with {len(item)} x {item_type}") else: print(f" - {item.__class__.__name__}") if objects is None: print(f"\nObjects: {objects}") else: print("\nObjects:") for item in objects: print(f" - {item[0]}: {item[1:]}") print(f"\nSpectral range: {field_range}") print(f"Companion labels: {companion_labels}") print(f"\nMass labels: {mass_labels}") print(f"Teff labels: {teff_labels}") if reddening is None: print(f"\nReddening: {reddening}") else: print("\nReddening:") for item in reddening: print(f" - {item}") print(f"\nFigure size: {figsize}") print(f"Limits x axis: {xlim}") print(f"Limits y axis: {ylim}") plt.rcParams["font.family"] = "serif" plt.rcParams["mathtext.fontset"] = "dejavuserif" # model_color = ("#234398", "#f6a432", "black") model_color = ( "tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple", "tab:brown", "tab:pink", "tab:olive", "tab:cyan", ) model_linestyle = ("-", "--", ":", "-.") if field_range is None: field_range = ("early M", "early Y") if (len(field_range[0]) == 1 and len(field_range[1]) != 1) or ( len(field_range[0]) != 1 and len(field_range[1]) == 1 ): raise ValueError( "The argument of 'field_range' should " "be a tuple with either the range of " "spectral type classes, for example " "('A', 'M'), or a tuple with the range " "of subclasses, for example ('early K', " "'late L')." ) if len(field_range[0]) == 1 and len(field_range[1]) == 1: check_subclass = False else: check_subclass = True isochrones = [] planck = [] models = [] empirical = [] spectra = [] for item in boxes: if isinstance(item, IsochroneBox): isochrones.append(item) elif isinstance(item, ColorColorBox): if item.object_type == "model": models.append(item) elif item.object_type == "spectra": spectra.append(item) elif item.library == "planck": planck.append(item) else: empirical.append(item) else: raise ValueError( f"Found a {type(item)} while only ColorColorBox and " "IsochroneBox objects can be provided to 'boxes'." ) fig = plt.figure(figsize=figsize) if empirical: gridsp = mpl.gridspec.GridSpec(3, 1, height_ratios=[0.2, 0.1, 4.0]) else: gridsp = mpl.gridspec.GridSpec(1, 1) gridsp.update(wspace=0.0, hspace=0.0, left=0, right=1, bottom=0, top=1) if empirical: ax1 = plt.subplot(gridsp[2, 0]) ax2 = plt.subplot(gridsp[0, 0]) else: ax1 = plt.subplot(gridsp[0, 0]) ax2 = None ax1.tick_params( axis="both", which="major", colors="black", labelcolor="black", direction="in", width=1, length=5, labelsize=12, top=True, bottom=True, left=True, right=True, ) ax1.tick_params( axis="both", which="minor", colors="black", labelcolor="black", direction="in", width=1, length=3, labelsize=12, top=True, bottom=True, left=True, right=True, ) ax1.xaxis.set_major_locator(MultipleLocator(0.5)) ax1.yaxis.set_major_locator(MultipleLocator(0.5)) ax1.xaxis.set_minor_locator(MultipleLocator(0.1)) ax1.yaxis.set_minor_locator(MultipleLocator(0.1)) ax1.set_xlabel(label_x, fontsize=14) ax1.set_ylabel(label_y, fontsize=14) if offset: ax1.get_xaxis().set_label_coords(0.5, offset[0]) ax1.get_yaxis().set_label_coords(offset[1], 0.5) else: ax1.get_xaxis().set_label_coords(0.5, -0.08) ax1.get_yaxis().set_label_coords(-0.12, 0.5) if xlim: ax1.set_xlim(xlim[0], xlim[1]) if ylim: ax1.set_ylim(ylim[0], ylim[1]) if models is not None: count = 0 model_dict = {} for j, item in enumerate(models): if item.library == "sonora-bobcat": model_key = item.library + item.iso_tag[-4:] else: model_key = item.library if model_key not in model_dict: model_dict[model_key] = [count, 0] count += 1 else: model_dict[model_key] = [ model_dict[model_key][0], model_dict[model_key][1] + 1, ] model_count = model_dict[model_key] if model_count[1] == 0: label = convert_model_name(item.library) if item.library == "sonora-bobcat": metal = float(item.iso_tag[-4:]) label += f", [M/H] = {metal}" if item.library == "zhu2015": ax1.plot( item.color1, item.color2, marker="x", ms=5, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color="gray", label=label, zorder=0, ) xlim = ax1.get_xlim() ylim = ax1.get_ylim() for i, teff_item in enumerate(item.sptype): teff_label = ( rf"{teff_item:.0e} $M_\mathregular{{Jup}}^{2}$ yr$^{{-1}}$" ) if item.color2[i] < ylim[1]: ax1.annotate( teff_label, (item.color1[i], item.color2[i]), color="gray", fontsize=8, ha="left", va="center", xytext=(item.color1[i] + 0.1, item.color2[i] - 0.05), zorder=3, ) else: ax1.plot( item.color1, item.color2, linestyle=model_linestyle[model_count[1]], lw=1.0, color=model_color[model_count[0]], label=label, zorder=0, ) if mass_labels is not None: interp_color1 = interp1d(item.mass, item.color1) interp_color2 = interp1d(item.mass, item.color2) if item.iso_tag in mass_labels: label_select = mass_labels[item.iso_tag] else: label_select = [] for i, mass_item in enumerate(label_select): mass_val = mass_item[0] mass_pos = mass_item[1] pos_color1 = interp_color1(mass_val) pos_color2 = interp_color2(mass_val) if mass_pos == "left": mass_ha = "right" mass_xytext = (pos_color1 - 0.05, pos_color2) else: mass_ha = "left" mass_xytext = (pos_color1 + 0.05, pos_color2) mass_label = str(int(mass_val)) + r" M$_\mathregular{J}$" xlim = ax1.get_xlim() ylim = ax1.get_ylim() if ( xlim[0] + 0.2 < pos_color1 < xlim[1] - 0.2 and ylim[0] + 0.2 < pos_color2 < ylim[1] - 0.2 ): ax1.scatter( pos_color1, pos_color2, c=model_color[model_count[0]], s=15, edgecolor="none", zorder=0, ) ax1.annotate( mass_label, (pos_color1, pos_color2), color=model_color[model_count[0]], fontsize=9, xytext=mass_xytext, ha=mass_ha, va="center", zorder=3, ) else: warnings.warn( f"Please use larger axes limits " f"to include the mass label for " f"{mass_val} Mjup." ) else: ax1.plot( item.color1, item.color2, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color=model_color[model_count[0]], label=None, zorder=0, ) if planck is not None: planck_count = 0 for j, item in enumerate(planck): if planck_count == 0: label = convert_model_name(item.library) ax1.plot( item.color1, item.color2, ls="--", linewidth=0.8, color="gray", label=label, zorder=0, ) if teff_labels is not None: interp_color1 = interp1d(item.sptype, item.color1) interp_color2 = interp1d(item.sptype, item.color2) if item.library in teff_labels: label_select = teff_labels[item.library] else: label_select = [] for i, teff_item in enumerate(label_select): if isinstance(teff_item, tuple): teff_val = teff_item[0] teff_pos = teff_item[1] else: teff_val = teff_item teff_pos = "right" if j == 0 or (j > 0 and teff_val < 20.0): pos_color1 = interp_color1(teff_val) pos_color2 = interp_color2(teff_val) if teff_pos == "left": teff_ha = "right" teff_xytext = (pos_color1 - 0.05, pos_color2) else: teff_ha = "left" teff_xytext = (pos_color1 + 0.05, pos_color2) teff_label = f"{int(teff_val)} K" xlim = ax1.get_xlim() ylim = ax1.get_ylim() if ( xlim[0] + 0.2 < pos_color1 < xlim[1] - 0.2 and ylim[0] + 0.2 < pos_color2 < ylim[1] - 0.2 ): ax1.scatter( pos_color1, pos_color2, c="gray", s=15, edgecolor="none", zorder=0, ) ax1.annotate( teff_label, (pos_color1, pos_color2), color="gray", fontsize=9, xytext=teff_xytext, zorder=3, ha=teff_ha, va="center", ) else: ax1.plot( item.color1, item.color2, ls="--", lw=0.5, color="gray", zorder=0 ) planck_count += 1 if spectra is not None: spectra_count = 0 for j, item in enumerate(spectra): if spectra_count == 0: label = convert_model_name(item.library) ax1.plot( item.color1, item.color2, ls="--", linewidth=0.8, color="tomato", label=label, zorder=0, ) if teff_labels is not None: interp_color1 = interp1d(item.sptype, item.color1) interp_color2 = interp1d(item.sptype, item.color2) if item.library in teff_labels: label_select = teff_labels[item.library] else: label_select = [] for i, teff_item in enumerate(label_select): if isinstance(teff_item, tuple): teff_val = teff_item[0] teff_pos = teff_item[1] else: teff_val = teff_item teff_pos = "right" if j == 0 or (j > 0 and teff_val < 20.0): pos_color1 = interp_color1(teff_val) pos_color2 = interp_color2(teff_val) if teff_pos == "left": teff_ha = "right" teff_xytext = (pos_color1 - 0.05, pos_color2) else: teff_ha = "left" teff_xytext = (pos_color1 + 0.05, pos_color2) teff_label = f"{int(teff_val)} K" xlim = ax1.get_xlim() ylim = ax1.get_ylim() if ( xlim[0] + 0.2 < pos_color1 < xlim[1] - 0.2 and ylim[0] + 0.2 < pos_color2 < ylim[1] - 0.2 ): ax1.scatter( pos_color1, pos_color2, c="tomato", s=15, edgecolor="none", zorder=0, ) ax1.annotate( teff_label, (pos_color1, pos_color2), color="tomato", fontsize=9, xytext=teff_xytext, zorder=3, ha=teff_ha, va="center", ) else: ax1.plot( item.color1, item.color2, ls="--", lw=0.5, color="tomato", zorder=0 ) spectra_count += 1 if empirical: cmap = plt.cm.viridis bounds, ticks, ticklabels = field_bounds_ticks(field_range, check_subclass) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) for item in empirical: sptype = item.sptype names = item.names color1 = item.color1 color2 = item.color2 if isinstance(sptype, list): sptype = np.array(sptype) if item.object_type in ["field", None]: indices = np.where(sptype != "None")[0] sptype = sptype[indices] color1 = color1[indices] color2 = color2[indices] spt_disc = sptype_to_index(field_range, sptype, check_subclass) _, unique = np.unique(color1, return_index=True) sptype = sptype[unique] color1 = color1[unique] color2 = color2[unique] spt_disc = spt_disc[unique] scat = ax1.scatter( color1, color2, c=spt_disc, cmap=cmap, norm=norm, s=50, alpha=0.7, edgecolor="none", zorder=2, ) cb = Colorbar( ax=ax2, mappable=scat, orientation="horizontal", ticklocation="top", format="%.2f", ) cb.ax.tick_params( width=1, length=5, labelsize=10, direction="in", color="black" ) cb.set_ticks(ticks) cb.set_ticklabels(ticklabels) cb.ax.minorticks_off() elif item.object_type == "young": if objects is not None: object_names = [] for obj_item in objects: object_names.append(obj_item[0]) indices = remove_color_duplicates(object_names, names) color1 = color1[indices] color2 = color2[indices] ax1.plot( color1, color2, marker="s", ms=4, linestyle="none", alpha=0.7, color="gray", markeredgecolor="black", label="Young/low-gravity", zorder=2, ) if isochrones: for item in isochrones: ax1.plot( item.colors[0], item.colors[1], linestyle="-", linewidth=1.0, color="black", ) if reddening is not None: for item in reddening: ext_1, ext_2 = calc_reddening( item[0], item[2], composition=item[3], structure="crystalline", radius_g=item[4], ) ext_3, ext_4 = calc_reddening( item[1], item[2], composition=item[3], structure="crystalline", radius_g=item[4], ) delta_x = ext_1 - ext_2 delta_y = ext_3 - ext_4 x_pos = item[5][0] + delta_x y_pos = item[5][1] + delta_y ax1.annotate( "", (x_pos, y_pos), xytext=(item[5][0], item[5][1]), fontsize=8, arrowprops={"arrowstyle": "->"}, color="black", zorder=3.0, ) x_pos_text = item[5][0] + delta_x / 2.0 y_pos_text = item[5][1] + delta_y / 2.0 vector_len = np.sqrt(delta_x**2 + delta_y**2) if item[3] == "MgSiO3": dust_species = r"MgSiO$_{3}$" elif item[3] == "Fe": dust_species = "Fe" if item[4].is_integer(): red_label = f"{dust_species} ({item[4]:.0f} µm)" else: red_label = f"{dust_species} ({item[4]:.1f} µm)" text = ax1.annotate( red_label, (x_pos_text, y_pos_text), xytext=(-7.0 * delta_y / vector_len, 7.0 * delta_x / vector_len), textcoords="offset points", fontsize=8.0, color="black", ha="center", va="center", ) ax1.plot([item[5][0], x_pos], [item[5][1], y_pos], "-", color="white") sp1 = ax1.transData.transform_point((item[5][0], item[5][1])) sp2 = ax1.transData.transform_point((x_pos, y_pos)) angle = np.degrees(np.arctan2(sp2[1] - sp1[1], sp2[0] - sp1[0])) text.set_rotation(angle) if objects is not None: for i, item in enumerate(objects): objdata = ReadObject(item[0]) objphot1 = objdata.get_photometry(item[1][0]) objphot2 = objdata.get_photometry(item[1][1]) objphot3 = objdata.get_photometry(item[2][0]) objphot4 = objdata.get_photometry(item[2][1]) if objphot1.ndim == 2: print( f"Found {objphot1.shape[1]} values for " f"filter {item[1][0]} of {item[0]} " f"so using the first magnitude: " f"{objphot1[0, 0]} +/- {objphot1[1, 0]}" ) objphot1 = objphot1[:, 0] if objphot2.ndim == 2: print( f"Found {objphot2.shape[1]} values for " f"filter {item[1][1]} of {item[0]} " f"so using the first magnitude: " f"{objphot2[0, 0]} +/- {objphot2[1, 0]}" ) objphot2 = objphot2[:, 0] if objphot3.ndim == 2: print( f"Found {objphot3.shape[1]} values for " f"filter {item[2][0]} of {item[0]} " f"so using the first magnitude: " f"{objphot3[0, 0]} +/- {objphot3[1, 0]}" ) objphot3 = objphot3[:, 0] if objphot4.ndim == 2: print( f"Found {objphot4.shape[1]} values for " f"filter {item[2][1]} of {item[0]} " f"so using the first magnitude: " f"{objphot4[0, 0]} +/- {objphot4[1, 0]}" ) objphot4 = objphot4[:, 0] color1 = objphot1[0] - objphot2[0] color2 = objphot3[0] - objphot4[0] error1 = np.sqrt(objphot1[1] ** 2 + objphot2[1] ** 2) error2 = np.sqrt(objphot3[1] ** 2 + objphot4[1] ** 2) if len(item) > 3 and item[3] is not None: kwargs = item[3] else: kwargs = { "marker": "o", "ms": 5.0, "color": "black", "mfc": "white", "mec": "black", "label": "Companions", } ax1.errorbar(color1, color2, xerr=error1, yerr=error2, zorder=3, **kwargs) if companion_labels: if len(item) > 3 and item[4] is not None: kwargs = item[4] else: kwargs = { "ha": "left", "va": "bottom", "fontsize": 8.5, "xytext": (5.0, 5.0), "color": "black", } ax1.annotate( objdata.object_name, (color1, color2), zorder=3, textcoords="offset points", **kwargs, ) handles, labels = ax1.get_legend_handles_labels() if legend is not None: handles, labels = ax1.get_legend_handles_labels() # Prevent duplicates by_label = dict(zip(labels, handles)) if handles: if isinstance(legend, (str, tuple)): ax1.legend( by_label.values(), by_label.keys(), loc=legend, fontsize=8.5, frameon=False, numpoints=1, ) else: ax1.legend(by_label.values(), by_label.keys(), **legend) if output is None: plt.show() else: print(f"\nOutput: {output}") plt.savefig(output, bbox_inches="tight") return fig